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Machine intelligence based hybrid classifier for spam detection and sentiment analysis of SMS messages

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Abstract

Short Message Service (SMS) on mobile phones has improved because of technological advancements and increases in content-based marketing where smart phones are frequently overburden with spam SMS. Spam messages are not important since they include virus and spyware. Several text classification methods have been suggested to address spam. However, none of these methods can guarantee a full spam-free solution since each filtering and modeling methodology has its own set of strengths and weaknesses. This paper suggests a hybrid classifier based on SMS spam classification and sentiment analysis. The datasets are pre-processed and Word2vec data augmentation is used to extract the features. Then, the features are fed to six various feature selection methods and equilibrium optimization (EO). Optimum components are then fed into a hybrid K-Nearest Neighbors (KNN) and support vector machine (SVM) classifier is to classify SMS messages. Further, to optimize the parameters of the network and to improve the accuracy, the optimization algorithm Rat Swarm Optimization (RSO) is used. Then, AFINN and SentiWordNet are used for sentiment analysis. This framework is evaluated on the three benchmark datasets; when comparing the performance of proposed method on the three dataset, spam assassin dataset achieves better spam detection accuracy of 99.82%.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

The authors would like to thank the National Institute of Technology Raipur, Chhattisgarh, India for providing infrastructure and facilities to carry out this research work.

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  1. Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Ulligaddala Srinivasarao & Aakanksha Sharaff

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  1. Ulligaddala Srinivasarao
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  2. Aakanksha Sharaff
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Correspondence to Ulligaddala Srinivasarao.

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Srinivasarao, U., Sharaff, A. Machine intelligence based hybrid classifier for spam detection and sentiment analysis of SMS messages. Multimed Tools Appl 82, 31069–31099 (2023). https://doi.org/10.1007/s11042-023-14641-5

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  • DOI: https://doi.org/10.1007/s11042-023-14641-5

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